This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)
Gene network modular-based classification of microarray samples
1 Department of Computer Science and Engineering, York University, Toronto, M3J 1P3, Canada
2 Prosserman Center for Health Research, Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, M5G 1X5, Canada
BMC Bioinformatics 2012, 13(Suppl 10):S17 doi:10.1186/1471-2105-13-S10-S17Published: 25 June 2012
Molecular predictor is a new tool for disease diagnosis, which uses gene expression to classify diagnostic category of a patient. The statistical challenge for constructing such a predictor is that there are thousands of genes to predict for the disease categories, but only a small number of samples are available.
We proposed a gene network modular-based linear discriminant analysis approach by integrating 'essential' correlation structure among genes into the predictor in order that the modules or cluster structures of genes, which are related to the diagnostic classes we look for, can have potential biological interpretation. We evaluated performance of the new method with other established classification methods using three real data sets.
Our results show that the new approach has the advantage of computational simplicity and efficiency with relatively lower classification error rates than the compared methods in many cases. The modular-based linear discriminant analysis approach induced in the study has the potential to increase the power of discriminant analysis for which sample sizes are small and there are large number of genes in the microarray studies.